Adaptive optimal operation of a parallel robotic liquid handling station

Tilman Barz, Andreas Sommer, Terrance Wilms, Peter Neubauer, Mariano Nicolas Cruz Bournazou

Publikation: Beitrag in FachzeitschriftArtikelBegutachtung


Results are presented from the optimal operation of a fully automated robotic liquid handling station where parallel experiments are performed for calibrating a kinetic fermentation model. To increase the robustness against uncertainties and/or wrong assumptions about the parameter values, an iterative calibration and experiment design approach is adopted. Its implementation yields a stepwise reduction of parameter uncertainties together with an adaptive redesign of reactor feeding strategies whenever new measurement information is available. The case study considers the adaptive optimal design of 4 parallel fed-batch strategies implemented in 8 mini-bioreactors. Details are given on the size and complexity of the problem and the challenges related to calibration of over-parameterized models and scarce and non-informative measurement data. It is shown how methods for parameter identifiability analysis and numerical regularization can be used for monitoring the progress of the experimental campaigns in terms of
Seiten (von - bis)765-770
PublikationsstatusVeröffentlicht - 2018

Research Field

  • Efficiency in Industrial Processes and Systems


  • Parallel robotic liquid handling station
  • E. coli kinetic model
  • Optimal experimental design for model calibration
  • Adaptive input design
  • Identifiability and ill-conditioning analysis


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